题　目：Data Preferences in Firm Learning: Evidence from an Online Auction Platform

嘉　宾：Xiaojie Li(李潇杰), Ph.D. Candidate, University of Rochester

主持人：张铄 副教授 金沙威尼斯欢乐娱人城

时　间：2024年10月18日（周五）13:30-15:00

地　点：金沙威尼斯欢乐娱人城 徐汇校区 金沙威尼斯欢乐娱人城B404

内容简介:

Governmental organizations have promoted data sharing across firms to expedite firms' learning to improve business decisions. However, current discussions have largely overlooked the possibility that firms may prefer their own data over others' data. This paper investigates such data preferences among firms, focusing on used-car auction sellers on Ali Auction, the largest online auction platform in China. These sellers primarily decide on auction timing, which is crucial on this platform as payoffs vary by hour. Despite being experienced local sellers before joining the platform, these sellers face national demand and competition in the online environment, creating the scope for learning. I develop a structural model of sellers' learning based on their own and others' data to optimize auction timing. The model estimates suggest that sellers' preferences for different data sources change with experience, with sellers relatively weighing their own data at 90% compared to 10% for others' data at the average level of experience. The counterfactual results show that data preferences are the main reason that prevents the sellers from achieving full potential profit. These findings have two implications for the platform. First, data sharing alone may not effectively guide sellers in selecting optimal auction timing. Second, the platform can leverage sellers' data preferences to guide new sellers to optimal timing early in their tenure, ensuring lasting benefits. Overall, the platform should play a coordinating role in helping sellers identify the best timing for their auctions.

演讲人简介：

Xiaojie Li is a Ph.D. candidate in Marketing at Simon Business School, University of Rochester. His research interests lie in platform economy, encompassing both digital platforms (e.g., online auction and social media platforms) and traditional offline platforms (e.g., bikeshare). Using structural modeling, causal inference, and machine learning, his current work focuses on how the platforms can leverage synergies either through spillover effects (e.g., data sharing or consumer usage of the products) or through the addition of new market participants to improve platform designs.

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